Methods based on 64-beam LiDAR can provide very precise 3D object detection. However, highly accurate LiDAR sensors are extremely costly: a 64-beam model can cost approximately USD 75,000. We previously proposed SLS–Fusion (sparse LiDAR and stereo fusion) to fuse low-cost four-beam LiDAR with stereo cameras that outperform most advanced stereo–LiDAR fusion methods. In this paper, and according to the number of LiDAR beams used, we analyzed how the stereo and LiDAR sensors contributed to the performance of the SLS–Fusion model for 3D object detection. Data coming from the stereo camera play a significant role in the fusion model. However, it is necessary to quantify this contribution and identify the variations in such a contribution with respect to the number of LiDAR beams used inside the model. Thus, to evaluate the roles of the parts of the SLS–Fusion network that represent LiDAR and stereo camera architectures, we propose dividing the model into two independent decoder networks. The results of this study show that—starting from four beams—increasing the number of LiDAR beams has no significant impact on the SLS–Fusion performance. The presented results can guide the design decisions by practitioners.

Today, the popularity of self-driving cars is growing at an exponential rate and is starting to creep onto the roads of developing countries. For autonomous vehicles to function, one of the essential features that needs to be developed is the ability to perceive their surroundings. To do this, sensors such as cameras, LiDAR, or radar are integrated to collect raw data. The objective of this paper is to evaluate a fusion solution of cameras and LiDARs (4 and 64 beams) for 3D object detection in foggy weather conditions. The data from the two input sensors are fused and an analysis of the contribution of each sensor on its own is then performed. In our analysis, we calculate average precision using the popular KITTI dataset, on which we have applied different intensities of fog (on a dataset we have called Multifog KITTI). The main results observed are as follows. Performances with stereo camera and 4 or 64 beams LiDAR are high (90.15%, 89.26%). Performance of the 4 beams LiDAR alone decreases sharply in foggy weather conditions (13.43%). Performance when using only a camera-based model remains quite high (89.36%). In conclusion, stereo cameras on their own are capable of detecting 3D objects in foggy weather with high accuracy and their performance slightly improves when used in conjunction with LIDAR sensors.

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